Both our style and content loss terms rely upon extracting a rich
feature representation from an particular spatial location. In this
work we use hypercolumns [1]}, [2]} extracted from a subset of layers of VGG16 trained
on ImageNet [3]}. Let
\(\Phi (X)^i\)  be the tensor of feature activations extracted from input
image \(X\)  by layer \(i\)  of network \(\Phi \) . Given the set of layer
indices \(l_1,..,l_L\)  we use bilinear upsampling to match the spatial
dimensions of \(\Phi (X)^{l_1}...\Phi (X)^{l_L}\)  to those of the original
image (\(X\) ), then concatenate all such tensors along the feature dimension. This yields a hypercolumn at each pixel which contains low-level edge and color features, mid-level texture features, and high-level semantic features [4]}. The content and style losses of STROTSS used all convolutional layers of VGG16 except layers 'conv4_2', 'conv4_3', 'conv5_2', and 'conv4_3', which we excluded due to memory constraints at the time of publication.
